process design; art or engineering (pragmatism or ... · process design; art or engineering?...

E D W I N Z O N D E R V A N

T U / E

J U N E 1 4 T H U V A .

Process Design; Art or Engineering?

...Pragmatism or fundamentals?

Outline

Introduction Who am I? What about TU/e and Chem. Eng? Process design

What is process design?

Process design in a nutshell (by example) Process creation/synthesis Reactor design Separations design Recycles Process simulation and optimization Heat and/or mass integration Economic evaluation

Current research Reactive distillation Biorefinery

2


Who am I?

25-6-2012

Born February 26th 1976 in Leeuwarden Performed bachelor in Process automation in Leeuwarden (1999)

Performed master in Chemical engineering at Groningen University (Master thesis topic: System Identification for control) (2003)

Performed doctorate at Twente University/Groningen University (2007)

Works currently as assistant professor at Eindhoven University


3

What about TU/e and Chem. Eng?

Three educational and research tracks: Chemistry

Material science

Process Engineering

Averagely 50 students enrol each year in our Bachelor and M.Sc. program (Majority moves to PE)

Averagely 15 students enrol in our post master “Process and Product Design”

Around 150 Ph.D. Students

Around 50 staff

4


What about TU/e and Chem. Eng?

Current classes in process and product design: 6BA75 – Process design, Bachelor course, 3 ECTS (40

students)

Case: Hydroalkylation of toluene

6BO06 – Product design and process management, Bachelor course, 4 ECTS (40 Students)

Case: Micellar catalysis of propylene oxide

6PE42 – Integrated process design, M.Sc. Course, 5 ECTS, (40 students)

Case: Production of paraphenylene diamine

5



In chemical engineering, process design is the design of processes for desired physical and/or chemical transformation of materials. The design starts at a conceptual level and ultimately ends in the form of fabrication and construction plants.

6


Nano Micro Meso Macro

http://en.wikipedia.org/wiki/Chemical_engineering


Several “process design” procedures: J.M. Douglas, Conceptual design of chemical processes, (1988)

Seider et al., Product and process design principles, (2010)

Grossmann et al, Systematic methods in chemical process design, (2004)

Bongers, Product Driven Proces Synthesis (PDPS)

All of them incorporate some kind of hierarchy with feedback. Passing through the hierarchy more level of detail is required.

Process design has many (multidisciplinary) facets; engineering, environment, business.

7


Douglas’ Process design hierarchy 8

Batch versus continuous

Input-output structure of the flowsheet

Recycle structure of the flowsheet

• Vapor recovery system • Liquid recovery system

General structure of the separation system

Heat exchanger network


Steps in Design and Retrofit 9


Assess Primitive Problem

Detailed Process Synthesis -Algorithmic

Methods

Development of Base-case

Plant-wide Controllability

Assessment

Detailed Design, Equipment sizing, Cap.

Cost Estimation, Profitability Analysis,

Optimization

Assess Primitive design problem 10

Process design begins with a primitive design problem that expresses the current situation and provides an opportunity to satisfy a societal need.

Normally, the primitive problem is examined by a small design team, who begins to assess its possibilities, to refine the problem statement, and to generate more specific problems: Raw materials - available in-house, can be purchased or need to be

manufactured? Scale of the process (based upon a preliminary assessment of the current

production, projected market demand, and current and projected selling prices)

Location for the plant

Refined through meetings with engineering technical management, business and marketing.

Brainstorming to generate alternatives


Important note... 11

We have many systematic tools to start designing a process, which seems to make it an Engineering discipline...

... But during the design process, it is creativity and knowledge beyound the Engineering community that leads to novel designs, which seems to make it an Art!

... But in this talk the emphasis will be on pragmatism, so on Engineering!!


The beginning... 12

... Most of the time it starts with something that seems to work in the lab!

The Alchemist, Adriaan van Ostade


Example: The toluene hydroalkylation process

Principle path:

C7H8 + H2 C6H6 + CH4

Benzene is one of the intermediates that can be converted to cyclohexane, and cyclohexane can be used to produce nylon

Side reaction:

2C6H6 C12H10 + H2

From lab. Data: irreversible reactions, no catalyst, temp ~1200-1270oF, conversion: 75% toluene/benzene, 25% benzene/biphenyl


Design objective

Design a plant with a capacity of 200 MMlb/year (based on a toluene conversion of 274 lbmol/hr and 330 days of operation annually)


Reaction operation of the hydroalkylation

Purge methane to avoid expensive separation of H2/CH4

Excess of H2 to prevent carbon deposition and to absorb heat


Adding the recycles

Note: not all amounts are known yet


Adding the separations

Note 1: Pressures not yet known Note 2: this is just one possible selection


Adding cooling and heating

Note: Heating and cooling added to alter temperature, pressure and liq./vap. Phase


Task Integration: adding the unit operations


Process simulation

After the generation of process flow-sheets you would like to analyze several things:

To solve mass/energy balances, phase equilibria, mass transfer, kinetics,

All with the aim of finding suitable operating conditions (Temperature, pressure, etc.)

P.S. Simulators are also developed and used with the aim of training operators.


Process simulation

For this we may use process simulators

Process simulators are mostly used for steady-state and scheduling calculations.

Process simulators can also be used for process dynamics and control;

economic evaluation and profitability analysis;

process optimization.


Why should we use simulators?

Solution of mass/energy balances in a simultaneous manner

Non-ideal thermodynamic models

Detailed (rigorous) unit operation models

Solve large sets of equations (typical ~10.000-100.000)


Software packages

There are many process simulator packages available, basically we divide them into two types:

Modular: Aspen Plus, HYSIS, CHEMCAD, PRO II, Unisim,...

Equation oriented (EO): Matlab, gproms, GAMS,...


Modular mode

Unit and thermodynamic models are self-contained subprograms (modules)

These flow-sheets are called at a higher level to converge the stream connectivity of the flow-sheet (e.g. Recycle streams)

Has a long history and is more popular for design work

Easy to construct and debug

however; it can be inflexible for various user specifications


Module based simulators


Equation oriented mode

Process equations (unit, connectivity, thermodynamic) are assembled and solved simultaneously.

Requires sophisticated numerical methods and software engineering concepts

Is primarily applied to online modelling and optimization.


EO simulators


Historical evolution

1950s: Unit stand-alone models execution in sequence to form a flow-sheet origin of sequential modular mode.

1960s: Sequential modular in-house flow-sheeting packages (petrochemical companies). Academic research for fundamentals of EO simulators.

1970s: Advanced methods for modular flow-sheets simultaneous modular flow-sheets. More general models and advanced numerical methods. Aspen (MIT).

1980s – 1990s: Considerable industrial development for equation oriented mode. User friendly interfaces and powerful algorithms. Vendor-supported software.

2000-today: Consideration of special issues and addition of the respective features/modules (e.g. Supply chain problems, advanced economic analysis, etc.)


Heat integration 29


Some streams in the process require heating and cooling.

Heat integration is concerned with finding the best connection between hot-, cold- and utility streams

Several procedures: Pinch analysis Graphical methods Algorithmic methods (e.g. With

math. optimization)

Heat and/or mass integration 30


Example of ethylene production process with heat integration

Safety considerations 31

Example Disaster 1 – Flixborough: 1st June 1974 http://www.hse.gov.uk/hid/land/comah/level3/5a591f6.htm 50 tons of cyclohexane were released from Nypro’s KA plant

(oxidation of cyclohexane) leading to release of vapor cloud and its detonation. Total loss of plant and death of 28 plant personnel.

Highly reactive system - conversions low, with large inventory in plant. Process involved six, 20 ton stirred-tank reactors.

– Discharge caused by failure of temporary pipe installed to replace cracked reactor.

– The so-called “dog-leg” was not able to contain the operating conditions of the process (10 bar, 150 oC)



Flixborough - What can we learn? Develop processes with low inventory, especially of flashing

fluids (“what you don’t have, can’t leak”) Before modifying process, carry out a systematic search for

possible cause of problem. Carry out HAZOP analysis Construct modifications to same standard as original plant. Use blast-resistant control rooms and buildings



Example Disaster 2 – Bhopal: 3rd December 1984 http://www.bhopal.com/chrono.htm Water leakage into MIC (Methyl isocyanate) storage tank leading

to boiling and release of 25 tons of toxic MIC vapor, killing more than 3,800 civilians, and injuring tens of thousands more.

MIC vapor released because the refrigeration system intended to cool the storage tank holding 100 tons of MIC had been shut down, the scrubber was not immediately available, and the flare was not in operation.

Bhopal - What can we learn?

– Avoid use of hazardous materials. Minimize stocks of hazardous materials (“what you don’t have, can’t leak”).

– Carry out HAZOP analysis. – Train operators not to ignore unusual readings. – Keep protective equipment in working order. – Control building near major hazards.


Process Control 34

Process design steady state

Dynamic behaviour unfavourable process characteristics.

Control systems should keep process at desired operating level for: Safety

Product specifications (quality and safety)

Environmental regulations

Operational constraints

Economics

Account for design errors


Plant wide control 35


Example of a plant wide control scheme for production of vinyl chloride

What to control and where to control without conflicts!

Economic evaluation 36

You use economic evaluation to determine from a set of alternative process designs that you have made whether or not they are (economically) feasible.

Cost accounting and profitability becomes more accurate as the process design becomes more detailed. But mind you that numbers can be easily 20-80% off!


Direct costs Indirect costs

Equipment

Piping

Civil & steel

Process control

Electrical

Insulation & paint

Engineering (conceptual,

basic, detailed)

Procurement

Construction & field

Supervision

Contract fees

37

Economic evaluation: Cost Accounting


Estimation often on basis of cost factors, e.g Lang factors Sizing often according to power laws

Economic evaluation: Manufacturing costs 38

Cost of Manufacture (COM) Feedstock

Utilities

Labor related operations

Maintanance


Economic evaluation: Profitability analysis

Approximate profitability measures: Return of investment (ROI)

Payback period (PBP)

Venture profit (VP)

Annualized Cost

Rigorous profitability measures Net Present value (NPV)

Time value of money

Cash Flow and Depreciation

39


Optimization for process design

Mathematical syntax:

min ( , )

. .

( , ) 0

( , ) 0

,

L U

L U

f x d

s t

c x d

g x d

x x x

d d d

x X d D

Objective: Economic or environmental criterion

Equality constraints: Mass & Energy balance, Equilibrium relations, etc.

Bound constraints: Equipment limits

Inequality constraints: Operating limits

Set over which the variables are defined: Continuous or discrete


40

Types of optimization problems

Optimization

Discrete Continuous

CLP MIP

MINLP

G-O

LP,QP,LCP

NLP

DFO

surrogate SA,GA

See: Biegler & Grossmann (2004) Grossmann & Biegler (2004)


41

Applications in Chem. Eng.

See: Biegler & Grossmann (2004) Grossmann & Biegler (2004)


42

Optimization approaches

Mathematical programming Simplex method, Lagrange multiplier method, SLP, SQP,

Branch & Bound, Disjunctive programming, constraint programming, etc.

Global optimization / Meta-heuristic Neural networks, fuzzy logic, ant colony, simulated annealing,

taboo search, genetic algorithms, etc.

Other Stochastic programming, multi-objective optimization, ...


43

Sustainable design of a reactive distillation column

Background Fatty acid esters of use to the food- and cosmetics industry.

Traditional production: Batch wise

Reactive distillation as PI option

Objective Develop and test a framework to identify and optimize relevant

operational and design parameters of an RD system

Reactive distillation

column


44

Sustainable design

Selected variables for multi criterion decision analysis: Number of stages

Operating pressure

Ratio

Conversion

Proposed framework by Bojarski

Simulator Optimizer Decision variables

Process variables

Ecoinvent

Pro

cess

va

ria

ble

s

Met

rics


45

Sustainable design

z1

z2

A

B

C D

E

E

A

B

C D

Objective space

Decision space

1 2min[ ( ) ( )]

. .

( ) 0

( ) 0

L U

Z x Z x

s t

c x

g x

x x x

x X Schilling et al. (1983)


46

Sustainable design

z1

z2

E

A

B

C

D

Objective space

Non-inferior set (Pareto Optimal, or non-dominant)

Ideal- or utopia point


47

Sustainable design

Environmental metrics

Economic metrics

Decision space

Multi criterion decision making

Catalyst loading is major player! Optimized settings for P, N, RR and X


48

Optimal design of a Biorefinery

Background Crude oil will deplete, alternative feedstock required, biomass

can be a renewable resource.

Objective Develop a process design optimization model that can assist in

selecting promising biomass conversion routes.


49

Proposed model

Superstructure and transhipment model

Sources Products (Sinks)

Processing steps

Splitting

Main reaction

Side reaction

Mixing

Reactants

Feed

Waste

Main product

By-product

Papoulias & Grossmann (1983) Dunn & Halwagi (1996)

50


Method

Mathematical program

0

0

0

1

2

1

),,(

),,(

),,,,(

..

),,(min

,,

k

ik

k

ik

k

ii

k

i

rk

m

rk

i

k

k

n

k

ik

n

fyg

fyg

SWMWh

y

ts

fyfwZ Objective function

Logical constraints

Component balances

Structural constraints

The details of this MINLP model can be found in the proceeding, it contains 452.179 single equations and 438.693 variables (containing only 68 decision variables) nonlinear terms can be linearized out!

51


Optimal design

Linearizations:

(1 ) (1 )

min{ , }

max{ , }

Lx z Ux

y U x z y L x

L y y

U y y

{0,1}

xy

x

y

Glover

Replace xy with z Non-convex/nonlinear

Convex/linear

... 0

L U

yF

F F F

... 0

L U

F

yF F yFStandard


52

Optimal design

Superstructure: all possible topologies. Outcome will be different for each objective


53

Concluding remarks 54

Process design ...cannot be explained in a single lecture!

...It has many facets

...it requires multiple disciplines

...there is a lot of uncertainty

...it is a hardcore engineering discipline

...(but it requires a lot of creativity too)


process design; art or engineering (pragmatism or ... · process design; art or engineering?...

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